Documentation

ojAlgo is documented three ways: the API reference on javadoc.io, the source itself, and the posts on this site. This page is the curated reading order through the posts — the ones that are still the best explanation of their topic, grouped so you can start where your problem is.

The example code behind every post is maintained — it is compiled and kept current with each ojAlgo release, so what you copy from a post works with the current version even where the surrounding prose reflects the version it was written for. All of it is collected on the Code Examples page.

Start here

• Get started — the Maven dependency and where the source lives.
• Questions, Complaints and Misconceptions — the recurring questions, answered properly. Read this before opening an issue.
• Common Mistake — the most common way ojAlgo ends up slow: converting to and from double[][] instead of using its own data structures.
• Changelog — what changed in every release.

Optimisation

Read the Mathematical Optimisation page first for what the solvers are and how ExpressionsBasedModel separates the model from the solver. Then:

• The Diet Problem — a first LP, formulated and solved end to end.
• The McNuggets Challenge — a first MIP: integer variables and what they change.
• Model and Solve the Traveling Salesman Problem — a harder MIP, with subtour elimination.
• MIP Strategy Configuration — how to steer the integer solver when the default strategy is not the right one.
• Gomory Mixed Integer Cuts — what cuts are and how the IntegerSolver uses them.
• Optimisation Model File Formats — reading and writing MPS, LP and EBM files. Note: since v57.1 ojAlgo also writes LP and MPS files, and detects the format from the file name; the post predates this.
• Hooking Your Solver to ojAlgo — how to make any solver usable from ExpressionsBasedModel. This is how the solver integrations are built.
• LP, QP & MIP on the JVM and the benchmarks — how the built-in solvers compare with the alternatives, on public test sets.

Linear algebra

Read the Linear Algebra page first. Then:

• Linear Algebra Introduction — the matrix types, how to create and combine them, and the decompositions.
• Sparse and Special Structure Matrices — the MatrixStore implementations, and how to write one that exploits your matrix’s structure.
• Generalised Eigenvalue Problems — the generalised forms and how to set them up.
• Iterative Solver Comparison — Jacobi, Gauss–Seidel and Conjugate Gradient for linear systems.
• StatQuest PCA Example, Image Processing using SVD and using FFT — worked examples of the decompositions and transforms.

Arrays, data and performance

• Working With Arrays — the array classes: dense, sparse, off-heap and file-backed, in one to N dimensions.
• 1BRC using ojAlgo — the one billion row challenge with those arrays.
• Introducing BatchNode — processing a dataset too large for memory, in stages.
• Controlling Concurrency — how to limit or direct ojAlgo’s multithreading.
• The Memory Estimator — estimating the footprint of an object graph.

Finance and machine learning

• The Financial Mathematics page, then Financial Time Series Data and GARCH.
• Artificial Neural Network Example v2 — the current neural network API, in one example. Mall Customer Segmentation — clustering.

Everything else

The full archive has every post, and Code Examples has every example program. If something here is wrong or missing, Discussions is the place to say so.